The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Plant disease detection is important in agriculture. Today an automated approach often involves classifying plant photos, which can be implemented by applying a convolutional neural network (CNN) having a plurality of convolutional layers. Some CNNs work together in a two-stage approach, where the first CNN is used to propose regions of interest within given images and a second CNN is then used to classify each proposed region. Some CNNs require all images to be of a fixed size. There are CNNs that can take images of various sizes and propose and classify regions of interest in one shot with superior performance. Given the distinct symptoms of plant diseases, it would be helpful to specifically configure and train such a CNN to classify plant photos and detect infection of plant diseases to promote plant health and growth.